{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DQN_Navigation_zw\n",
    "\n",
    "---\n",
    "\n",
    "In this notebook, you will learn how to use the Unity ML-Agents environment for the first project of the [Deep Reinforcement Learning Nanodegree](https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893).\n",
    "\n",
    "### 1. Start the Environment\n",
    "\n",
    "We begin by importing some necessary packages.  If the code cell below returns an error, please revisit the project instructions to double-check that you have installed [Unity ML-Agents](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Installation.md) and [NumPy](http://www.numpy.org/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "INFO:unityagents:\n'Academy' started successfully!\nUnity Academy name: Academy\n        Number of Brains: 1\n        Number of External Brains : 1\n        Lesson number : 0\n        Reset Parameters :\n\t\t\nUnity brain name: BananaBrain\n        Number of Visual Observations (per agent): 0\n        Vector Observation space type: continuous\n        Vector Observation space size (per agent): 37\n        Number of stacked Vector Observation: 1\n        Vector Action space type: discrete\n        Vector Action space size (per agent): 4\n        Vector Action descriptions: , , , \n"
    }
   ],
   "source": [
    "from unityagents import UnityEnvironment\n",
    "env = UnityEnvironment(file_name=\"./Banana_Linux/Banana.x86_64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Requirement already satisfied: box2d in /home/sjtuer/anaconda3/lib/python3.7/site-packages\n\u001b[33mYou are using pip version 19.0.3, however version 20.1.1 is available.\nYou should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\nRequirement already satisfied: pyvirtualdisplay in /home/sjtuer/anaconda3/lib/python3.7/site-packages\nRequirement already satisfied: EasyProcess in /home/sjtuer/anaconda3/lib/python3.7/site-packages (from pyvirtualdisplay)\n\u001b[33mYou are using pip version 19.0.3, however version 20.1.1 is available.\nYou should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
    }
   ],
   "source": [
    "import gym\n",
    "!pip3 install box2d\n",
    "import random\n",
    "import torch\n",
    "import numpy as np\n",
    "from collections import deque\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "!python -m pip install pyvirtualdisplay\n",
    "from pyvirtualdisplay import Display\n",
    "display = Display(visible=0, size=(1400, 900))\n",
    "display.start()\n",
    "\n",
    "is_ipython = 'inline' in plt.get_backend()\n",
    "if is_ipython:\n",
    "    from IPython import display\n",
    "\n",
    "plt.ion()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Environments contain **_brains_** which are responsible for deciding the actions of their associated agents. Here we check for the first brain available, and set it as the default brain we will be controlling from Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the default brain\n",
    "brain_name = env.brain_names[0]\n",
    "brain = env.brains[brain_name]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Examine the State and Action Spaces\n",
    "\n",
    "The simulation contains a single agent that navigates a large environment.  At each time step, it has four actions at its disposal:\n",
    "- `0` - walk forward \n",
    "- `1` - walk backward\n",
    "- `2` - turn left\n",
    "- `3` - turn right\n",
    "\n",
    "The state space has `37` dimensions and contains the agent's velocity, along with ray-based perception of objects around agent's forward direction.  A reward of `+1` is provided for collecting a yellow banana, and a reward of `-1` is provided for collecting a blue banana. \n",
    "\n",
    "Run the code cell below to print some information about the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Number of agents: 1\nNumber of actions: 4\nStates look like: [1.      0.      0.      0.      0.84408 0.      0.      1.      0.      0.07485 0.      1.      0.      0.\n 0.25755 1.      0.      0.      0.      0.74177 0.      1.      0.      0.      0.25855 0.      0.      1.\n 0.      0.09356 0.      1.      0.      0.      0.31969 0.      0.     ]\nStates have length: 37\n"
    }
   ],
   "source": [
    "np.set_printoptions(precision=5, linewidth=120)\n",
    "# reset the environment\n",
    "env_info = env.reset(train_mode=True)[brain_name]\n",
    "\n",
    "# number of agents in the environment\n",
    "print('Number of agents:', len(env_info.agents))\n",
    "\n",
    "# number of actions\n",
    "action_size = brain.vector_action_space_size\n",
    "print('Number of actions:', action_size)\n",
    "\n",
    "# examine the state space \n",
    "state = env_info.vector_observations[0]\n",
    "print('States look like:', state)\n",
    "state_size = len(state)\n",
    "print('States have length:', state_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Take Random Actions in the Environment\n",
    "\n",
    "In the next code cell, you will learn how to use the Python API to control the agent and receive feedback from the environment.\n",
    "\n",
    "Once this cell is executed, you will watch the agent's performance, if it selects an action (uniformly) at random with each time step.  A window should pop up that allows you to observe the agent, as it moves through the environment.  \n",
    "\n",
    "Of course, as part of the project, you'll have to change the code so that the agent is able to use its experience to gradually choose better actions when interacting with the environment!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "env_info = env.reset(train_mode=False)[brain_name] # reset the environment\n",
    "state = env_info.vector_observations[0]            # get the current state\n",
    "score = 0                                          # initialize the score\n",
    "while True:\n",
    "    action = np.random.randint(action_size)        # select an action\n",
    "    env_info = env.step(action)[brain_name]        # send the action to the environment\n",
    "    next_state = env_info.vector_observations[0]   # get the next state\n",
    "    reward = env_info.rewards[0]                   # get the reward\n",
    "    done = env_info.local_done[0]                  # see if episode has finished\n",
    "    score += reward                                # update the score\n",
    "    state = next_state                             # roll over the state to next time step\n",
    "    if done:                                       # exit loop if episode finished\n",
    "        break\n",
    "    \n",
    "print(\"Score: {}\".format(score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When finished, you can close the environment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Train the agent wiht DQN!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "cuda:0\n"
    }
   ],
   "source": [
    "from dqn_agent import Agent\n",
    "agent = Agent(state_size=37, action_size=4, seed=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Episode 100 \tAverage Score: 0.49\nEpisode 200 \tAverage Score: 3.62\nEpisode 300 \tAverage Score: 7.95\nEpisode 400 \tAverage Score: 10.35\nEpisode 500 \tAverage Score: 12.42\nEpisode 523 \tAverage Score: 13.00\nEnvironment solved in 423 episodes! \t Average Score: 13.00\n\nTotal Training time = 26.1 min\n"
    }
   ],
   "source": [
    "import time\n",
    "def dqn(n_episodes=2000, max_t=1000, eps_start=1.0, eps_end=0.01, eps_decay=0.995):\n",
    "    \"\"\"Deep Q-Learning.\n",
    "    \n",
    "    Params\n",
    "    ======\n",
    "        n_episodes (int): maximum number of training episodes\n",
    "        max_t (int): maximum number of timesteps per episode\n",
    "        eps_start (float): starting value of epsilon, for epsilon-greedy action selection\n",
    "        eps_end (float): minimum value of epsilon\n",
    "        eps_decay (float): multiplicative factor (per episode) for decreasing epsilon\n",
    "    \"\"\"\n",
    "    scores = [] # list containing socres from each episode\n",
    "    scores_window = deque(maxlen=100) # last 100 scores\n",
    "    eps = eps_start\n",
    "    for i_episode in range(1, n_episodes+1):\n",
    "        \n",
    "        env_info = env.reset(train_mode=True)[brain_name] # reset the environment\n",
    "        state = env_info.vector_observations[0] # get the current state\n",
    "        \n",
    "        score = 0\n",
    "        \n",
    "        for t in range(max_t):\n",
    "            action = agent.act(state, eps)\n",
    "            env_info = env.step(action)[brain_name] # interact with the environment based on the current chosen action\n",
    "            next_state = env_info.vector_observations[0] # get the next state\n",
    "            reward = env_info.rewards[0] # get the reward\n",
    "            done = env_info.local_done[0] \n",
    "            \n",
    "            agent.step(state, action, reward, next_state, done)\n",
    "            state = next_state\n",
    "            score += reward\n",
    "            if done:\n",
    "                break\n",
    "\n",
    "        scores_window.append(score) # save most recent score\n",
    "        scores.append(score) # save most recent score\n",
    "        eps = max(eps_end, eps_decay * eps) # decreate epsilon\n",
    "        print('\\rEpisode {} \\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_window)), end='')\n",
    "        # print(i_episode % 100)\n",
    "        if i_episode % 100 == 0:\n",
    "            print('\\rEpisode {} \\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_window)))\n",
    "\n",
    "        if np.mean(scores_window) >= 13.0:\n",
    "            print('\\nEnvironment solved in {:d} episodes! \\t Average Score: {:.2f}'.format(i_episode-100, np.mean(scores_window)))\n",
    "            torch.save(agent.q_network_local.state_dict(), 'checkpoint.pth')\n",
    "            break\n",
    "    return scores\n",
    "\n",
    "start_time = time.time() # Monitor Training Time  \n",
    "scores = dqn()\n",
    "print(\"\\nTotal Training time = {:.1f} min\".format((time.time()-start_time)/60))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# plot the scores \n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "plt.plot(np.arange(len(scores)), scores)\n",
    "plt.title('Score (Rewards)')\n",
    "plt.ylabel(\"Score\")\n",
    "plt.xlabel(\"Episode # \")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "env.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "drlnd",
   "language": "python",
   "name": "drlnd"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}